Alibaba/Dense

AlibabaQwen3 1.7B

Qwen3 1.7B — efficient small model with thinking/reasoning mode.

chatreasoningThinkingTool Use
1.7B
Parameters
32K
Context length
19
Benchmarks
6
Quantizations
300K
HF downloads
Architecture
Dense
Released
2025-04-28
Layers
28
KV Heads
8
Head Dim
128
Family
qwen

Quantization Options

QuantBitsVRAMQuality
Q4_K_M4.891.5 GBgood
Q5_K_S5.571.7 GBgood
Q5_K_M5.71.7 GBgood
Q6_K6.561.9 GBexcellent
Q8_08.52.3 GBlossless
FP16163.9 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN QWEN3 1.7B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (19)

MATH-50089.4
MATH72.0
HumanEval68.0
IFEval65.0
AIME38.7
AA Math38.7
GPQA Diamond35.6
LiveCodeBench30.8
BigCodeBench27.0
τ²-Bench21.6
IFBench21.1
MMLU-PRO19.8
BBH18.3
AA Intelligence8.0
SciCode6.9
HLE4.8
MUSR4.0
AA Coding1.4
GPQA0.0

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run qwen3:1.7b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Find the best GPU for Qwen3 1.7B

Build Hardware for Qwen3 1.7B

Qwen3 1.7B — efficient small model with thinking/reasoning mode.

▸ SPEC SHEET

Qwen3 1.7B1.7B Dense.

▸ SPECIFICATIONS
PARAMETERS
1.7B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, reasoning
RELEASE DATE
2025-04-28
PROVIDER
Alibaba
FAMILY
qwen
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.891.5 GB94%
Q5_K_S5.571.7 GB96%
Q5_K_M5.71.7 GB96%
Q6_K6.561.9 GB97%
Q8_08.52.3 GB100%
FP16163.9 GB100%
§ 01BENCHMARK SCORES
HumanEval68.0
MMLU-PRO19.8
MATH72.0
IFEval65.0
BBH18.3
GPQA0.0
MUSR4.0
BigCodeBench27.0
GPQA Diamond35.6
LiveCodeBench30.8
AIME38.7
MATH-50089.4
HLE4.8
AA Intelligence8.0
AA Coding1.4
AA Math38.7
aa_ifbench21.1
aa_tau221.6
aa_scicode6.9
§ 02RUN COMMAND

Run Qwen3 1.7B locally with Ollama — needs 1.5 GB VRAM at Q4_K_M:

$ollama run qwen3:1.7b